Bioinformatics and data mining in proteomics

Proteomic studies involve the identification as well as qualitative and quantitative comparison of proteins expressed under different conditions, and elucidation of their properties and functions, usually in a large-scale, high-throughput format. The high dimensionality of data generated from these studies will require the development of improved bioinformatics tools and data-mining approaches for efficient and accurate data analysis of biological specimens from healthy and diseased individuals. Mining large proteomics data sets provides a better understanding of the complexities between the normal and abnormal cell proteome of various biological systems, including environmental hazards, infectious agents (bioterrorism) and cancers. This review will shed light on recent developments in bioinformatics and data-mining approaches, and their limitations when applied to proteomics data sets, in order to strengthen the interdependence between proteomic technologies and bioinformatics tools.     

Bioinformatics in mass spectrometry data analysis for proteomics studies

Mass spectrometry is a technique widely employed for the identification and characterization of proteins. The role of bioinformatics is fundamental for the elaboration of mass spectrometry data due to the amount of data that this technique can produce. To process data efficiently, new software packages and algorithms are continuously being developed to improve protein identification and characterization in terms of high-throughput and statistical accuracy. However, many limitations exist concerning bioinformatics spectral data elaboration. This review aims to critically cover the recent and future developments of new bioinformatics approaches in mass spectrometry data analysis for proteomics studies.     

生物信息学及其在蛋白质组学中的应用

随着基因组学和蛋白质组学的发展,生物信息学在数据处理中的应用已经越来越广泛。作为数据处理中越来越重要的分析手段,蛋白质组学数据库是蛋白质组学的主要内容之一。本文分别从生物信息学的蛋白质双向电泳数据库和基于蛋白质质谱结果的数据库两个方面,概述了发展中的蛋白质数据库的最新动态和有关信息,同时对主要的热门蛋白质组学数据库站点和资源进行了评价和分析。     

Bioinformatics analysis of mass spectrometry-based proteomics data sets

Proteomics has made tremendous progress, attaining throughput and comprehensiveness so far only seen in genomics technologies. The consequent avalanche of proteome level data poses great analytical challenges for downstream interpretation. We review bioinformatic analysis of qualitative and quantitative proteomic data, focusing on current and emerging paradigms employed for functional analysis, data mining and knowledge discovery from high resolution quantitative mass spectrometric data. Many bioinformatics tools developed for microarrays can be reused in proteomics, however, the uniquely quantitative nature of proteomics data also offers entirely novel analysis possibilities, which directly suggest and illuminate biological mechanisms.     

Bioinformatics approaches in clinical proteomics

Protein expression profiling is increasingly being used to discover, validate and characterize biomarkers that can potentially be used for diagnostic purposes and to aid in pharmaceutical development. Correct analysis of data obtained from these experiments requires an understanding of the underlying analytic procedures used to obtain the data, statistical principles underlying high-dimensional data and clinical statistical tools used to determine the utility of the interpreted data. This review summarizes each of these steps, with the goal of providing the nonstatistician proteomics researcher with a working understanding of the various approaches that may be used by statisticians. Emphasis is placed on the process of mining high-dimensional data to identify a specific set of biomarkers that may be used in a diagnostic or other assay setting.     

Informatics and data management in proteomics

Proteomics has become dominated by large amounts of experimental data and interpreted results. This experimental data cannot be effectively used without understanding the fundamental structure of its information content and representing that information in such a way that knowledge can be extracted from it. This review explores the structure of this information with regard to three fundamental issues: the extraction of relevant information from raw data, the scale of the projects involved and the statistical significance of protein identification results.     

细菌生物被膜核心胞外多糖靶向水解酶的生物信息挖掘北大核心

【目的】胞外多糖是生物被膜不可或缺的重要成分,在细菌致病和耐药过程中发挥着重要作用。运用酶制剂针对生物被膜的核心胞外多糖进行靶向清除,能够从根本上破坏细菌生物被膜的核心骨架,有助于战胜细菌生物被膜导致的危害。【方法】本研究针对常见致病菌生物被膜核心胞外多糖Pel、Psl、褐藻胶、N-乙酰氨基葡萄糖(Poly-β(1,6)-N-acetyl-D-glucosamine,PNAG)和纤维素,基于NCBI数据库中丰富的基因序列信息,筛选靶向生物被膜核心胞外多糖的水解酶,进一步运用phyre2、SWISS-MODEL等生物信息工具,分析了这些水解酶的理化性质、遗传进化、功能域及三维结构。【结果】筛选获得了153个靶向生物被膜核心胞外多糖的水解酶及其序列信息。其中,靶向Pel胞外多糖的水解酶共30个,属于糖苷水解酶114家族(glycoside-hydrolase family GH114);靶向Psl胞外多糖的水解酶共25个,属于糖苷水解酶超家族(glyco_hydro super family);靶向褐藻胶胞外多糖的水解酶共33个,属于褐藻胶裂解酶超家族(Alg Lyase superfamily);靶向PNAG胞外多糖的水解酶共30个,属于糖苷水解酶13家族(glycoside-hydrolase family GH13);靶向纤维素胞外多糖的水解酶共35个,属于糖苷水解酶8家族(glycosyl hydrolases family GH8)。【结论】这些水解酶菌具备靶向瓦解生物被膜核心胞外多糖的潜力,亟待进一步开发与应用。本研究提供了迄今为止最为全面的生物被膜核心胞外多糖水解酶序列组成及生物信息,为生物被膜的精准预防和靶向控制奠定扎实的数据基础。     

Metaproteomic data analysis at a glance: advances in computational microbial community proteomics

Introduction: Within the last decade, the study of microbial communities has gained increasing research interest also driven by the recognition of the important role of these consortia in human health and disease. Metaproteomics, the analysis of the entire set of proteins from all microorganisms present in one ecosystem, has become a prominent technique for studying the relation between taxonomic diversity and functional profile of microbial communities.

Areas covered: The aim of this review is to address opportunities and challenges of metaproteomics from a computational perspective. Appealing to an audience of microbial ecologists and proteomic researchers alike, we provide an overview on state-of-the-art software and databases by which metaproteome data can be readily analyzed.

Expert commentary: While tailored protein databases, combined search algorithms and iterative workflows are means to improve the identification yield, software tools for taxonomic and functional analysis are challenged by the vast amount of unannotated sequences in metaproteomics.     

生物信息学

生物信息学是采用计算机技术和信息论方法研究生命科学中各种生物信息的表达;采集,储存,传递,检索,分析和解读的科学,是现代生命科学与信息科学,计算机科学,数学,统计学,物理学,化学等学科相互渗透和高度交叉形成的学科,本文简要介绍了现代生物信息学的主要研究领域。     

Electrocardiogram data mining based on frame classification by dynamic time warping matching

This paper presents an electrocardiogram (ECG) data mining scheme based on the ECG frame classification realised by a dynamic time warping (DTW) matching technique, which has been used successfully in speech recognition. We use the DTW to classify ECG frames because ECG and speech signals have similar non-stationary characteristics. The DTW mapping function is obtained by searching the frame from its end to start. A threshold is setup for DTW matching residual either to classify an ECG frame or to add a new class. Classification and establishment of a template set are carried out simultaneously. A frame is classified into a category with a minimal residual and satisfying a threshold requirement. A classification residual of 1.33% is achieved by the DTW for a 10-min ECG recording.     

可变剪接与疾病的生物信息学研究概况

可变剪接是真核基因转录后期的重要调控机制,它使得同一条蛋白质编码基因能够产生多种转录体,极大的扩展了遗传信息的应用.研究发现,可变剪接与人类疾病有着密切的联系.错误的剪接会导致疾病,增加疾病的易感性与病变程度,甚至直接导致癌变.现对可变剪接调控机制与疾病的生物信息学研究进展进行综述.     

The HUPO proteomics standards initiative--easing communication and minimizing data loss in a changing world

The amount of data currently being generated by proteomics laboratories around the world is increasing exponentially, making it ever more critical that scientists are able to exchange, compare and retrieve datasets when re-evaluation of their original conclusions becomes important. Only a fraction of this data is published in the literature and important information is being lost every day as data formats become obsolete. The Human Proteome Organisation Proteomics Standards Initiative (HUPO-PSI) was tasked with the creation of data standards and interchange formats to allow both the exchange and storage of such data irrespective of the hardware and software from which it was generated. This article will provide an update on the work of this group, the creation and implementation of these standards and the standards-compliant data repositories being established as result of their efforts.     

A practical guide to interpreting and generating bottom-up proteomics data visualizations

Mass-spectrometry based bottom-up proteomics is the main method to analyze proteomes comprehensively and the rapid evolution of instrumentation and data analysis has made the technology widely available. Data visualization is an integral part of the analysis process and it is crucial for the communication of results. This is a major challenge due to the immense complexity of MS data. In this review, we provide an overview of commonly used visualizations, starting with raw data of traditional and novel MS technologies, then basic peptide and protein level analyses, and finally visualization of highly complex datasets and networks. We specifically provide guidance on how to critically interpret and discuss the multitude of different proteomics data visualizations. Furthermore, we highlight Python-based libraries and other open science tools that can be applied for independent and transparent generation of customized visualizations. To further encourage programmatic data visualization, we provide the Python code used to generate all data figures in this review on GitHub ( https://github.com/MannLabs/ProteomicsVisualization ).     

Protein Information and Knowledge Extractor: Discovering biological information from proteomics data

One of the main goals in proteomics is to solve biological and molecular questions regarding a set of identified proteins. In order to achieve this goal, one has to extract and collect the existing biological data from public repositories for every protein and afterward, analyze and organize the collected data. Due to the complexity of this task and the huge amount of data available, it is not possible to gather this information by hand, making it necessary to find automatic methods of data collection. Within a proteomic context, we have developed Protein Information and Knowledge Extractor (PIKE) which solves this problem by automatically accessing several public information systems and databases across the Internet. PIKE bioinformatics tool starts with a set of identified proteins, listed as the most common protein databases accession codes, and retrieves all relevant and updated information from the most relevant databases. Once the search is complete, PIKE summarizes the information for every single protein using several file formats that share and exchange the information with other software tools. It is our opinion that PIKE represents a great step forward for information procurement and drastically reduces manual database validation for large proteomic studies. It is available at http://proteo.cnb.csic.es/pike .     

Region-resolved proteomics profiling of monkey heart

Nonhuman primates (NHPs) play an indispensable role in biomedical research because of their similarities in genetics, physiological, and neurological function to humans. Proteomics profiling of monkey heart could reveal significant cardiac biomarkers and help us to gain a better understanding of the pathogenesis of heart disease. However, the proteomic study of monkey heart is relatively lacking. Here, we performed the proteomics profiling of the normal monkey heart by measuring three major anatomical regions (vessels, valves, and chambers) based on iTRAQ-coupled LC-MS/MS analysis. Over 3,200 proteins were identified and quantified from three heart tissue samples. Furthermore, multiple bioinformatics analyses such as gene ontology analysis, protein–protein interaction analysis, and gene-diseases association were used to investigate biological network of those proteins from each area. More than 60 genes in three heart regions are implicated with heart diseases such as hypertrophic cardiomyopathy, heart failure, and myocardial infarction. These genes associated with heart disease are mainly enriched in citrate cycle, amino acid degradation, and glycolysis pathway. At the anatomical level, the revelation of molecular characteristics of the healthy monkey heart would be an important starting point to investigate heart disease. As a unique resource, this study can serve as a reference map for future in-depth research on cardiac disease-related NHP model and novel biomarkers of cardiac injury.     

Bioinformatics: living on the edge

A report on the 11th European Conference on Computational Biology (ECCB), Basel, Switzerland, September 9-12, 2012.     

耐药结核分枝杆菌基因组信息挖掘

<正>由于结核分枝杆菌的耐药性问题,使结核病这个古老的传染病死灰复燃,并成为全球性的严重公关问题。从1998年首个结核分枝杆菌(H37Rv)全基因组完成图的获得[1],到近年来采用新一代测序技术对多个菌株进行高通量的基因组测序与分析,对结核分枝杆菌进化及耐药机制的认识不断深入。关于结核分枝杆菌菌株的基因组比较分析有多篇报道,包括对中国12个省来源的161株结核分枝杆菌